Roll Up Your Sleeves: Working with a Collaborative and Engaging Task-Oriented Dialogue System

TL;DR

TacoBot is a user-centered, open-source task-oriented dialogue system designed to guide users through complex tasks like cooking, utilizing advanced neural models and data augmentation for engaging and efficient assistance.

Contribution

The paper introduces TacoBot, a novel collaborative dialogue system with integrated data augmentation strategies, advancing practical deployment in real-world tasks.

Findings

Secured third place in Alexa Prize TaskBot Challenge

Demonstrated effective use of LLM-based data augmentation

Provided an open-source framework for task-oriented dialogue

Abstract

We introduce TacoBot, a user-centered task-oriented digital assistant designed to guide users through complex real-world tasks with multiple steps. Covering a wide range of cooking and how-to tasks, we aim to deliver a collaborative and engaging dialogue experience. Equipped with language understanding, dialogue management, and response generation components supported by a robust search engine, TacoBot ensures efficient task assistance. To enhance the dialogue experience, we explore a series of data augmentation strategies using LLMs to train advanced neural models continuously. TacoBot builds upon our successful participation in the inaugural Alexa Prize TaskBot Challenge, where our team secured third place among ten competing teams. We offer TacoBot as an open-source framework that serves as a practical example for deploying task-oriented dialogue systems.